Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	131,068
2	South Dakota	127,133
3	Rhode Island	119,494
4	Utah	115,875
5	Arizona	112,419
6	Tennessee	111,564
7	Oklahoma	107,377
8	Arkansas	106,868
9	Iowa	106,777
10	Wisconsin	106,089
11	Nebraska	104,195
12	Kansas	102,095
13	Alabama	100,703
14	South Carolina	100,602
15	Mississippi	99,119
16	Indiana	98,841
17	Idaho	96,033
18	Nevada	95,469
19	Wyoming	94,116
20	Illinois	94,004
21	Montana	93,672
22	Georgia	92,666
23	Louisiana	92,605
24	Texas	91,786
25	Kentucky	91,513
26	California	90,432
27	Delaware	89,426
28	New Jersey	89,223
29	Florida	88,971
30	New Mexico	88,370
31	Minnesota	86,051
32	New York	84,846
33	Massachusetts	84,518
34	Missouri	84,334
35	Ohio	82,886
36	North Carolina	82,568
37	Alaska	79,597
38	Connecticut	79,271
39	Colorado	75,048
40	West Virginia	73,681
41	Pennsylvania	73,293
42	Virginia	67,620
43	Michigan	64,821
44	Maryland	63,331
45	District of Columbia	57,646
46	New Hampshire	55,591
47	Washington	45,138
48	Puerto Rico	42,019
49	Oregon	36,956
50	Maine	33,299
51	Vermont	24,494
52	Hawaii	19,421

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Arkansas	427
2	New York	382
3	New Jersey	362
4	Delaware	336
5	Rhode Island	303
6	South Carolina	303
7	Connecticut	250
8	Georgia	234
9	Massachusetts	220
10	Colorado	214
11	Florida	197
12	Texas	197
13	District of Columbia	188
14	Pennsylvania	187
15	North Carolina	180
16	Virginia	177
17	Alaska	174
18	Vermont	171
19	New Hampshire	170
20	Mississippi	162
21	Oklahoma	157
22	Kentucky	153
23	West Virginia	151
24	Utah	146
25	Louisiana	137
26	South Dakota	136
27	Tennessee	135
28	Arizona	133
29	Minnesota	133
30	Alabama	130
31	Ohio	128
32	Maryland	124
33	Montana	119
34	Nebraska	118
35	Maine	115
36	Illinois	110
37	Iowa	110
38	Michigan	110
39	Indiana	105
40	California	102
41	Idaho	102
42	Nevada	96
43	Wisconsin	95
44	New Mexico	89
45	Washington	78
46	Oregon	76
47	Puerto Rico	75
48	Kansas	72
49	Wyoming	69
50	Missouri	63
51	North Dakota	61
52	Hawaii	42

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,620
2	New York	2,428
3	Rhode Island	2,375
4	Massachusetts	2,342
5	Mississippi	2,244
6	Arizona	2,195
7	Connecticut	2,145
8	South Dakota	2,134
9	Louisiana	2,071
10	Alabama	2,025
11	North Dakota	1,935
12	Pennsylvania	1,879
13	Indiana	1,870
14	Illinois	1,796
15	New Mexico	1,779
16	Arkansas	1,739
17	Iowa	1,734
18	South Carolina	1,662
19	Tennessee	1,658
20	Michigan	1,653
21	Kansas	1,628
22	Nevada	1,609
23	Georgia	1,585
24	Texas	1,520
25	Ohio	1,484
26	Delaware	1,460
27	District of Columbia	1,443
28	Florida	1,443
29	Missouri	1,366
30	California	1,328
31	Maryland	1,303
32	West Virginia	1,283
33	Montana	1,270
34	Wisconsin	1,205
35	Minnesota	1,162
36	Wyoming	1,159
37	Nebraska	1,139
38	Oklahoma	1,131
39	North Carolina	1,076
40	Kentucky	1,074
41	Colorado	1,049
42	Idaho	1,045
43	Virginia	1,028
44	New Hampshire	860
45	Washington	661
46	Puerto Rico	637
47	Utah	605
48	Oregon	526
49	Maine	522
50	Alaska	395
51	Vermont	328
52	Hawaii	307

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Virginia	22
2	Oklahoma	13
3	New Mexico	6
4	California	5
5	Florida	5
6	Massachusetts	5
7	New York	5
8	South Carolina	5
9	Texas	5
10	Alabama	4
11	Alaska	4
12	District of Columbia	4
13	Georgia	4
14	Kentucky	4
15	Mississippi	4
16	Ohio	4
17	Rhode Island	4
18	Arizona	3
19	Indiana	3
20	New Jersey	3
21	South Dakota	3
22	Utah	3
23	Connecticut	2
24	Illinois	2
25	Louisiana	2
26	Maryland	2
27	Michigan	2
28	North Carolina	2
29	Pennsylvania	2
30	Tennessee	2
31	Colorado	1
32	Delaware	1
33	Idaho	1
34	Minnesota	1
35	Nebraska	1
36	Nevada	1
37	North Dakota	1
38	Puerto Rico	1
39	West Virginia	1
40	Hawaii	0
41	Iowa	0
42	Kansas	0
43	Maine	0
44	Missouri	0
45	Montana	0
46	New Hampshire	0
47	Oregon	0
48	Vermont	0
49	Washington	0
50	Wisconsin	0
51	Wyoming	0
52	Arkansas	-17

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	337,898	1	99
Chattahoochee	Georgia	282,387	2	99
Bent	Colorado	265,196	3	99
Lincoln	Arkansas	242,015	4	99
Dewey	South Dakota	239,647	5	99
Davidson	Tennessee	129,077	226	92
Richland	South Carolina	99,683	1085	65
York	South Carolina	95,011	1295	58
Orange	California	82,315	1876	40
Pierce	Washington	42,689	2900	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Galax city	Virginia	7,563	3	99
Emporia city	Virginia	7,295	4	99
Gregory	South Dakota	6,930	5	99
Davidson	Tennessee	1,249	1925	38
Orange	California	1,235	1950	37
Richland	South Carolina	1,198	2000	36
York	South Carolina	1,185	2016	35
Pierce	Washington	612	2697	14

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.